Hotspot and molecular descriptors for estimating change in off-rate.

<p>The hotspot descriptors designed in this work are benchmarked against a set of 110 molecular descriptors; both in their ability to estimate Δlog₁₀(<i>k_off</i>) and in their ability to detect stabilizing mutations of <b>Δlog₁₀(</b><b><i>k_off</i></b><b>)</b> <−1. The performance measures shown here enable us to assess the raw predictive power of the descriptors independent of any learning models. Green and black bars highlight descriptors from the hotspot and molecular descriptor sets respectively. (A) Comparison of the distribution of the absolute PCC values for the hotspot descriptors designed in this work against that for the molecular descriptors. The related list of descriptor names and their respective PCCs is found in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003216#pcbi.1003216.s015" target="_blank">Text S5</a>. (B) Top 10 hotspot descriptors and top 10 molecular descriptor according to absolute PCC with experimental Δlog₁₀(<i>k_off</i>). (C) Mann Whitney U-Test rankings for all descriptors where values are ranked according to −log₁₀(pval) and represent the discrimination ability of the descriptors for the detection of stabilizing mutants (<b>Δlog₁₀(</b><b><i>k_off</i></b><b>)</b> <−1) from neutral to destabilizing mutants (<b>Δlog₁₀(</b><b><i>k_off</i></b><b>)</b> >0) (Referred to as CDS1). This dataset contains 31 stabilizing mutants and 503 neutral to destabilizing mutants. (D) Matthew's Correlation Coefficient (MCC) rankings for all descriptors on same dataset. (E) and (F) are identical to (C) and (D) except that results are for off-rates that satisfy |<b>Δlog₁₀(</b><b><i>k_off</i></b><b>)|</b> >1. This dataset contains 31 stabilizing mutants and 213 destabilizing mutants (referred to as CDS2).